from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2022-12-20 14:02:39.175529
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 20, Dec, 2022
Time: 14:02:44
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -51.2625
Nobs: 876.000 HQIC: -51.5655
Log likelihood: 11571.0 FPE: 3.34119e-23
AIC: -51.7531 Det(Omega_mle): 3.01671e-23
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.296312 0.049705 5.961 0.000
L1.Burgenland 0.105440 0.034015 3.100 0.002
L1.Kärnten -0.106809 0.018267 -5.847 0.000
L1.Niederösterreich 0.214388 0.071372 3.004 0.003
L1.Oberösterreich 0.087155 0.067569 1.290 0.197
L1.Salzburg 0.249832 0.036119 6.917 0.000
L1.Steiermark 0.030212 0.047438 0.637 0.524
L1.Tirol 0.127446 0.038614 3.301 0.001
L1.Vorarlberg -0.061885 0.033195 -1.864 0.062
L1.Wien 0.062105 0.060341 1.029 0.303
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.063709 0.102210 0.623 0.533
L1.Burgenland -0.009939 0.069948 -0.142 0.887
L1.Kärnten 0.049279 0.037564 1.312 0.190
L1.Niederösterreich -0.172969 0.146766 -1.179 0.239
L1.Oberösterreich 0.362456 0.138946 2.609 0.009
L1.Salzburg 0.286043 0.074274 3.851 0.000
L1.Steiermark 0.108632 0.097549 1.114 0.265
L1.Tirol 0.319135 0.079404 4.019 0.000
L1.Vorarlberg 0.024663 0.068261 0.361 0.718
L1.Wien -0.025096 0.124083 -0.202 0.840
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.199717 0.025760 7.753 0.000
L1.Burgenland 0.090055 0.017629 5.108 0.000
L1.Kärnten -0.009127 0.009467 -0.964 0.335
L1.Niederösterreich 0.267586 0.036989 7.234 0.000
L1.Oberösterreich 0.112783 0.035019 3.221 0.001
L1.Salzburg 0.052724 0.018719 2.817 0.005
L1.Steiermark 0.015794 0.024585 0.642 0.521
L1.Tirol 0.102285 0.020012 5.111 0.000
L1.Vorarlberg 0.056726 0.017204 3.297 0.001
L1.Wien 0.112465 0.031273 3.596 0.000
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.104384 0.026448 3.947 0.000
L1.Burgenland 0.047616 0.018100 2.631 0.009
L1.Kärnten -0.016963 0.009720 -1.745 0.081
L1.Niederösterreich 0.197306 0.037977 5.195 0.000
L1.Oberösterreich 0.277896 0.035954 7.729 0.000
L1.Salzburg 0.117766 0.019219 6.128 0.000
L1.Steiermark 0.100400 0.025242 3.978 0.000
L1.Tirol 0.126766 0.020546 6.170 0.000
L1.Vorarlberg 0.069866 0.017663 3.956 0.000
L1.Wien -0.025865 0.032108 -0.806 0.420
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.131456 0.047736 2.754 0.006
L1.Burgenland -0.054124 0.032668 -1.657 0.098
L1.Kärnten -0.037001 0.017544 -2.109 0.035
L1.Niederösterreich 0.166785 0.068546 2.433 0.015
L1.Oberösterreich 0.133626 0.064894 2.059 0.039
L1.Salzburg 0.290775 0.034689 8.382 0.000
L1.Steiermark 0.034326 0.045559 0.753 0.451
L1.Tirol 0.161909 0.037085 4.366 0.000
L1.Vorarlberg 0.108085 0.031881 3.390 0.001
L1.Wien 0.065708 0.057952 1.134 0.257
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.060433 0.037818 1.598 0.110
L1.Burgenland 0.038365 0.025881 1.482 0.138
L1.Kärnten 0.049900 0.013899 3.590 0.000
L1.Niederösterreich 0.227628 0.054304 4.192 0.000
L1.Oberösterreich 0.269814 0.051411 5.248 0.000
L1.Salzburg 0.058978 0.027482 2.146 0.032
L1.Steiermark -0.006741 0.036093 -0.187 0.852
L1.Tirol 0.157454 0.029380 5.359 0.000
L1.Vorarlberg 0.069262 0.025257 2.742 0.006
L1.Wien 0.075556 0.045911 1.646 0.100
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.185659 0.045390 4.090 0.000
L1.Burgenland 0.018170 0.031063 0.585 0.559
L1.Kärnten -0.060258 0.016682 -3.612 0.000
L1.Niederösterreich -0.093816 0.065177 -1.439 0.150
L1.Oberösterreich 0.175874 0.061704 2.850 0.004
L1.Salzburg 0.060979 0.032984 1.849 0.064
L1.Steiermark 0.229764 0.043320 5.304 0.000
L1.Tirol 0.488735 0.035262 13.860 0.000
L1.Vorarlberg 0.051018 0.030314 1.683 0.092
L1.Wien -0.054386 0.055104 -0.987 0.324
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.158327 0.051490 3.075 0.002
L1.Burgenland -0.000004 0.035237 -0.000 1.000
L1.Kärnten 0.066451 0.018923 3.512 0.000
L1.Niederösterreich 0.201202 0.073935 2.721 0.007
L1.Oberösterreich -0.070298 0.069996 -1.004 0.315
L1.Salzburg 0.220399 0.037417 5.890 0.000
L1.Steiermark 0.112629 0.049141 2.292 0.022
L1.Tirol 0.084460 0.040001 2.111 0.035
L1.Vorarlberg 0.123655 0.034387 3.596 0.000
L1.Wien 0.104879 0.062508 1.678 0.093
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.358783 0.030442 11.786 0.000
L1.Burgenland 0.007027 0.020833 0.337 0.736
L1.Kärnten -0.025502 0.011188 -2.279 0.023
L1.Niederösterreich 0.230199 0.043713 5.266 0.000
L1.Oberösterreich 0.154871 0.041384 3.742 0.000
L1.Salzburg 0.052958 0.022122 2.394 0.017
L1.Steiermark -0.016411 0.029054 -0.565 0.572
L1.Tirol 0.122351 0.023650 5.173 0.000
L1.Vorarlberg 0.071338 0.020331 3.509 0.000
L1.Wien 0.045916 0.036957 1.242 0.214
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.038612 0.160287 0.181561 0.168826 0.142227 0.128037 0.065713 0.218510
Kärnten 0.038612 1.000000 0.001510 0.132194 0.026950 0.099334 0.432564 -0.049272 0.100947
Niederösterreich 0.160287 0.001510 1.000000 0.346239 0.170669 0.312758 0.128624 0.191895 0.339862
Oberösterreich 0.181561 0.132194 0.346239 1.000000 0.234660 0.341866 0.178518 0.180061 0.271875
Salzburg 0.168826 0.026950 0.170669 0.234660 1.000000 0.153401 0.137776 0.153168 0.139801
Steiermark 0.142227 0.099334 0.312758 0.341866 0.153401 1.000000 0.160073 0.148171 0.093993
Tirol 0.128037 0.432564 0.128624 0.178518 0.137776 0.160073 1.000000 0.123272 0.163159
Vorarlberg 0.065713 -0.049272 0.191895 0.180061 0.153168 0.148171 0.123272 1.000000 0.019065
Wien 0.218510 0.100947 0.339862 0.271875 0.139801 0.093993 0.163159 0.019065 1.000000